---
title: "DLT Efficiency Metric"
path: "/contents/resources/python-scripts/dlt-efficiency-metric"
version: "1.3.1"
author: "Hydrate"
createdAt: "2026-01-27T11:46:50.534Z"
updatedAt: "2026-07-17T03:08:04.867Z"
---

# DLT Efficiency Metric

<Infobox>
| Type | Python script · experimental metric |
| Measures | Ecosystem output (GitHub repos) per unit of attention (Google Trends) |
| Language | Python 3 |
| Dependencies | `requests`, `pytrends` |
| Data sources | [GitHub Search API](https://docs.github.com/en/rest/search/search#search-repositories) · [Google Trends](https://trends.google.com) |
| Sample | 14 July 2023 snapshot (Ethereum, Solana, Avalanche, Cardano, Radix) |
| Related | [Radix vs Ethereum](/contents/tech/comparisons/radix-vs-ethereum) · [Scrypto](/contents/tech/core-protocols/scrypto-programming-language) · [Proof of Work](/contents/resources/python-scripts/proof-of-work) |
</Infobox>

This script treats DLTs like Radix and [Ethereum](https://ethereum.org) like engines and attempts to measure their efficiency in creating utility.

## **Introduction**

Mechanical efficiency is measured by dividing Work or Power Output by Power Input:

*η* = *W* / *I* = Work (or Power) Output ÷ Power Input

The following method substitutes Github repos for Work and Google Trends volume as a measure of Power Input. The assumption is that amount of ecosystem activity will be roughly reflected in the search volume.

In this version, the number of Github repos for Radix and Ethereum is determined using the comparable search terms “[Scrypto](/contents/tech/core-protocols/scrypto-programming-language) Radix” and “[Solidity](https://soliditylang.org) Ethereum”. The search volumes for “[Scrypto](/contents/tech/core-protocols/scrypto-programming-language) Radix” are not large enough to register so instead we have used “XRD Radix” and “ETH Ethereum” to maintain equivalence and eliminate searches for other uses of the term ‘Radix’.

### **Results**

The figures below are a single snapshot taken on 14 July 2023 and are preserved as a record of that run. Both inputs move continuously — GitHub repository counts grow and Google Trends rebases its scores against whatever window is queried — so re-running the script today will produce different absolute numbers and ratios.

| **DLT** | **Repos** | **Efficiency (η) (23/07/14)** | **Vs Ethereum** | **Search terms (Github)** | **Search terms (Google Trends)** |
| [**Ethereum**](/contents/tech/comparisons/radix-vs-ethereum) | 12461 | **8.45** | **1** | “Solidity Ethereum” | “ETH Ethereum” |
| **[Solana](https://solana.com)** | 1076 | **0.79** | **11x** | “Rust Solana” | “SOL Solana” |
| **Avalanche** | 88 | **0.09** | **91x** | “Solidity Avalanche” | “AVAX Avalanche” |
| **Cardano** | 176 | **0.09** | **97x** | “Plutus Cardano” | “ADA Cardano” |
| **Radix** | 21 | **0.07** | **123x** | “Scrypto Radix” | “XRD Radix” |

## **Method & Python Script**

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`python3 DLTefficiency.py`